Network Biology: Applications in medicine and biotechnology: Dissertation

Erno Lindfors

Research output: ThesisDissertationCollection of Articles


The concept of systems biology emerged over the last decade in order to address advances in experimental techniques. It aims to characterize biological systems comprehensively as a complex network of interactions between the system's components. Network biology has become a core research domain of systems biology. It uses a graph theoretic approach. Many advances in complex network theory have contributed to this approach, and it has led to practical applications spanning from disease elucidation to biotechnology during the last few years. Herein we applied a network approach in order to model heterogeneous biological interactions. We developed a system called megNet for visualizing heterogeneous biological data, and showed its utility by biological network visualization examples, particularly in a biomedical context. In addition, we developed a novel biological network analysis method called Enriched Molecular Path detection method (EMPath) that detects phenotypic specific molecular paths in an integrated molecular interaction network. We showed its utility in the context of insulitis and autoimmune diabetes in the non-obese diabetic (NOD) mouse model. Specifically, ether phosholipid biosynthesis was down-regulated in early insulitis. This result was consistent with a previous study in which serum metabolite samples were taken from children who later progressed to type 1 diabetes and from children who permanently remained healthy. As a result, ether lipids were diminished in the type 1 diabetes progressors. Also, in this thesis we performed topological calculations to investigate whether ubiquitous complex network properties are present in biological networks. Results were consistent with recent critiques of the ubiquitous complex network properties describing the biological networks, which gave motivation to tailor another method called Topological Enrichment Analysis for Functional Subnetworks (TEAFS). This method ranks topological activities of modules of an integrated biological network under a dynamic response to external stress. We showed its utility by exposing an integrated yeast network to oxidative stress. Results showed that oxidative stress leads to accumulation of toxic lipids.
Original languageEnglish
QualificationDoctor Degree
Awarding Institution
  • Aalto University
  • Orešič, Matej, Supervisor, External person
Award date4 Nov 2011
Place of PublicationEspoo
Print ISBNs978-951-38-7758-3
Electronic ISBNs978-951-38-7759-0
Publication statusPublished - 2011
MoE publication typeG5 Doctoral dissertation (article)


  • network biology
  • systems biology
  • biological data visualization
  • type 1 diabetes
  • oxidative stress
  • graph theory
  • network topology
  • ubiquitous complex network properties


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